Geospatial Mapping of Soil Organic Carbon Using Regression Kriging and Remote Sensing

Geo-spatial mapping of soil organic carbon using regression kriging was performed for Lalo khala sub-watershed (a part of Solani watershed) located in western Uttar Pradesh, India. Soil organic carbon was predicted using eight predictor variables derived from the advanced space borne thermal emission and reflection radiometer satellite images and digital elevation model. The soil organic carbon was determined in 248 soil samples collected randomly within a 300 m2 grid overlaid on the study area. Out of the eight predictor variables used in simple regression, the normalized difference vegetation index has the maximum correlation with the soil organic carbon (0.64) followed by vegetation temperature condition index (0.60), brightness index (− 0.60), greenness index (0.57) and wetness index (0.51). Standardized principle components of the predictor variables were used in the prediction model so as to address the multicollinearity problem. The regression kriging predicted SOC value ranged from 0.19 to 1.93% with a mean value of 0.64 and standard deviation of 0.29. The SOC values were higher in upper piedmont with moderate forest followed by Siwalik hills while low values were found in the upper alluvial plains. The RMSE of the predicted SOC map was only 0.196 indicating the closeness of predicted values to the observed values. Regression kriging predicted SOC map can be used for spatial agriculture planning and consider as an ideal input for spatially distributed models. The higher efforts for its preparation are justified when quality, spatial distribution and accuracy are considered.